An implantable medical device (IMD) is implanted in a patient to monitor, among other things, electrical activity of a heart and to deliver appropriate electrical therapy, as required. IMDs include pacemakers, cardioverters, defibrillators, implantable cardioverter defibrillators (ICD), and the like. The electrical therapy produced by an IMD may include pacing pulses, cardioverting pulses, and/or defibrillator pulses to reverse arrhythmias (e.g., tachycardias and bradycardias) or to stimulate the contraction of cardiac tissue (e.g., cardiac pacing) to return the heart to its normal sinus rhythm. These pulses are referred to as stimulus or stimulation pulses.
In order to determine when stimulus pulses are to be applied to the heart, the IMDs identify cardiac events of the heart and, based on the cardiac events, supply or withhold the stimulus pulses to the heart. By way of example, the cardiac events may include cardiac signal waveform segments, segments of cardiac signals between waveform segments, heart rates, and the like.
The accurate identification of cardiac events is desirable to ensure that stimulus pulses are applied to the heart when needed and are withheld when unnecessary. Some known IMDs use time-domain processing techniques to identify cardiac events. For example, the IMDs may compare the cardiac signals to a threshold and classify the cardiac signals as indicative of a cardiac event every time the cardiac signals extend above the threshold. But, identifying cardiac events based on comparisons between cardiac signals and a threshold can be prone to errors. For example, in some patients a QRS complex may be misidentified as a T-wave, and vice-versa.
The battery energy reserves of some known IMDs limit the amount of computational resources that are implemented in analysis methods that may be used by the IMDs to identify cardiac events based on cardiac signals. For example, the battery energy reserves of some known IMDs are too small to permit the IMDs to engage in costly frequency domain processing techniques such as Fourier transforms and wavelet transforms. As a result, some known IMDs are limited to the simple time-domain processing techniques such as comparing cardiac signals to a single threshold.
A need exists for an IMD that more accurately identifies cardiac events while avoiding costly processing techniques that consume the battery energy reserves of the IMDs.